I’m a PhD Student in the Cheriton School of Computer Science at the University of Waterloo supervised by Prof. Xi He. My research interests lie in Differentially Private learning algorithms with a focus on Federated Learning and Databases. Before this, I was a Master’s student supervised by Prof. Xi He and Prof. Helen Chen.
I was opportune to spend some beautiful years at IIIT Bhubaneswar and work with Prof. Anjali Mohapatra on Genetic Clustering Algorithms in Bioinformatics.
When I’m not pondering on a piece of code to complete my project, you could find me sweating it out in a game of basketball, spending hours on board games or simply hanging out with my friends. Exploring novel concepts excites me the most. Going to new places and meeting new people keeps me going but at the end of the day, I’d rather come back to sipping my cup of masala tea with my younger brother at home. My Myers-Briggs Type Indicator(MBTI) is ENFJ [Extraverted, iNtuitive, Feeling, Judging].
Interests. Differential Privacy, Deep Learning, Databases
News
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[15.8.2024] Differentially Private Data Generation with Missing Data is accepted to VLDB'24.
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[21.10.2023] I received the prestigious OGS/QEII-GSST scholarship for CAD $15k from the University of Waterloo.
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[18.10.2023] Our paper ‘Differentially Private Data Generation with Missing Data’ is now on Arxiv.
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[18.08.2023] New paper ‘Benchmarks for Private Image Classification’ accepted at TPDP'23.
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[23.03.2023] I am now a Vector institute affiliated graduate student.
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[15.06.2022] New paper ‘Differentially Private Data Generation with Missing Data’ accepted at TPDP'22.
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[03.02.2022] Selected for AAAI'22 Student Scholarship award.
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[01.12.2021] The Role of Adaptive Optimizers for Honest Hyperparameter Tuning is accepted to AAAI'22.
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[09.11.2021] Our paper ‘The Role of Adaptive Optimizers for Honest Hyperparameter Tuning’ is now on Arxiv.
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[1.09.2021] Started as a PhD student.
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[23.07.21] Presented our paper ‘The Role of Adaptive Optimizers for Honest Private Hyperparameter Selection’ at TPDP'21.
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[08.07.21] GooseDP our team at Differential Privacy Temporal Mapo Challenge won the 5th prize. The open source version of our code is here.
Publications
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Differentially Private Data Generation with Missing Data,
VLDB 2024.
[code]
Shubhankar Mohapatra, Jianqiao Zong, Florian Kerschbaum, Xi He
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The Role of Adaptive Optimizers for Honest Hyperparameter Tuning,
AAAI 2022.
[code]
[poster]
[slides]
Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar
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Kamino: Constraint-Aware Differentially Private Data Synthesis,
VLDB 2021.
[code]
Chang Ge, Shubhankar Mohapatra, Xi He, Ihab F. Ilyas
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Federated Deep Learning Architecture for Personalized Healthcare,
Public Health and Informatics.
Helen Chen, Shubhankar Mohapatra, George Michalopoulos, Xi He, Ian McKillop
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Triclustering of Gene Expression Microarray Data Using Coarse-Grained Parallel Genetic Algorithm,
ICICCT 2019.
Shubhankar Mohapatra, Moumita Sarkar, Anjali Mohapatra, Bhawani Sankar Biswal
Workshop
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Rethinking Benchmarks for Private Image Classification,
Theory and Practice of Differential Privacy. TPDP 2023.
Sabrina Mokhtari, Shubhankar Mohapatra, Sara Kodeiri, Florian Tramèr, Gautam Kamath
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KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments,
International Workshop on Big Data for Financial News and Data. IEEE BigData 2019.
Shubhankar Mohapatra, Nauman Ahmed, Paulo Alencar
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The Role of Adaptive Optimizers for Honest Hyperparameter Selection,
Theory Practices of Differential Privacy. ICML 2021.
Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, Om Thakkar